Abstract
COVID-19 induced acute respiratory distress syndrome (ARDS) could have two different phenotypes, which was reported to have different response and outcome to the typical ARDS positive end-expiration pressure (PEEP) treatment. The identification of the different phenotypes in terms of the recruitability can help improve the patient outcome. In this contribution we conducted alveolar overdistention and collapse analysis with the long term electrical impedance tomography monitoring data on two severe COVID-19 pneumonia patients. The result showed different patient reactions to the PEEP trial, revealed the progressive change in the patient status, and indicted a possible phenotype transition in one patient. It might suggest that EIT can be a practical tool to identify phenotypes and to provide progressive information of COVID-19 pneumonia.
【저자키워드】 Locomotion, Respiration, biomedical imaging systems, decision support systems for the control of physiological, clinical variables, control of voluntary movements, 【초록키워드】 COVID-19, Treatment, ARDS, Severe COVID-19 pneumonia, Trial, Pneumonia, outcome, Electrical impedance tomography, Patient, phenotype, information, patients, acute respiratory distress, Analysis, PEEP, Phenotypes, reaction, syndrome, help, positive, alveolar, Recruitability, IMPROVE, identify, reported, the patient, conducted, 【제목키워드】 COVID-19, detection, impedance, collapse, Estimated,